data governance

Why Is Data Governance Important for Data Analysts?

Data Governance is the practice of governing the usability, security, and integrity of data in a business. Enterprises constitute and follow internal policies to ensure their data is reliable, secure, and free from external interference.

Effective data governance policies pave the way for better data analysis, which leads to better sales and enhanced customer satisfaction.

Organizations collect large amounts of data internally and externally to fuel necessary services. The process of data governance aims at helping maintain the quality of Big Data throughout its life cycle.

 Companies employ highly professional data analysts and data scientists to ensure effective compliance with data governance policies and standards. This team is also responsible for managing risks, reducing costs, and keeping the data secure.

Why is data governance important?

It is no secret that data is the new gold mine in the modern era. Enterprises rely heavily on their collected data for every meaningful step they take as a business.

Businesses manage their daily decisions by following the proper protocols and suitably collecting, controlling, and eradicating inconsistencies in data. Wrong data management can force companies to make wrong decisions, eventually affecting the end-user experience.

For instance, consider a company that delivers services to its customers. The company has siloed departments like logistics, sales, and customer service to manage their day-to-day requests better. Each of these departments works independently with no data governance policies in play.

A minor problem like a mismatch of customer names within different departments can delay services and issue resolution. Identifying bottlenecks within departments can turn into a painstaking experience, especially without proper data governance policies and procedures.

Role of Data Governance Within Enterprises

Effective policies and data governance are essential to minimize data inconsistencies between different departments and sections.

Every business unit can agree upon a similar data convention agreed with the governing body.

The governance unit constantly works on minimizing errors in their databases by infusing policies and procedures. Such procedures improve the data quality manifold.

Multiple security processes are devised and implemented by the data governance team. These processes ensure third parties and internal employees aren’t able to misuse the internal data repositories.

The decision-making process becomes more accessible due to improved data quality and data literacy. Enterprises can leverage their high-quality data to utilize other services like real-time intelligence and AI efficiently by using effective business intelligence tools.

Data protection laws like GDPR and CCPA are hard to comply with. These policies ensure that an enterprise toes the line with data governance laws prevalent in the region where they function.

An effective governance structure ensures that multiple layers of protection secure the stored data. This minimizes data leaks as there is no unauthorized data access.

Less time gets wasted in resolving data inconsistencies across the database, thereby making the processes efficient. This way, data governance policies decrease data management costs and increase cost-effectiveness within the organization.

Who’s responsible for data governance?

Data governance teams are a common feature within enterprises; these are becoming a standard function, given the growing demands for proper policies. These teams include business executives, IT professionals, and Data management experts familiar with the organization’s data domains.

Some of the key participants in a data governance team include:

Chief Data Officer

Chief Data Officer (CDO) is one of the most senior executives in the data governance team. A CDO secures approval, funding, and staffing for the program and plays a crucial role in setting up the team and monitoring the progress. Only another C-suite executive can replace a CDO in case of their unavailability.

Data Governance Manager

A CDO or an equivalent officer handles the responsibility of managing the data governance program. In other cases, a particular data governance manager is appointed to manage the team and oversee the data accessibility, usage and data availability. This team coordinates processes, heads meetings, and conducts training sessions while keeping track of metrics that flow within the system.

Data Governance Committee

The data governance committee is responsible for making policies and defining data conventions within the enterprise. The data governance teams and managers further implement these policies.

This committee also resolves conflicts between different business units over data-related issues. Additionally, this committee ensures that the policies are compliant with the laws in the region.

Data Stewards

Workers with knowledge of data assets and domains are usually appointed as data stewards in a company. A data steward is responsible for ensuring the correct implementation of policies issued by the data governance committee.

This role can be full-time or part-time, depending on the scale of the organization. There is a mix of IT and business data stewards in some organizations.

The data governance team also includes data architects, data analysts, and data modelers. Some enterprises also train their data analysts with governance policies and safe practices.

Data Governance Implementation

The first step to implementing proper data governance begins with identifying the owners of different data assets and involving them in the governance program.

A CDO or equivalent C-suite officer is appointed to take the lead and create the program’s structure, employing different team officers and formalizing the data governance committee.

The committee then forms various rules, regulations, and policies that define the authorization of every request within the organization.

The entire framework is developed with defined SOPs and documents to mark the entry point of all data. The process of storing data to protect it is also documented.

What’s included in a data governance structure?

Data mapping and classification: All the data collected in the system is mapped and classified into different categories according to an organization’s needs. This eases data asset documentation and separates classified data into multiple databases. Various data policies are created to govern the security of each data category.

Business Glossary

A business glossary is designed to highlight the definitions of business concepts and standard terms. This helps in resolving conflicts easily and forming uniform definitions across the company. The business glossary is also helpful to train employees with the updated data policies within the organization.

Data Catalog

The data catalog is an indexed inventory of all available data assets, along with details about their lineage, search functions, and tools. All the policy details are fed into the data catalog, and automated processes implement them internally.

Do you need data governance?

A few years ago, data governance was a way to comply with the governance rules, laws, and regulations. This scenario has changed, and every organization can benefit from properly implementing policies.

Let’s take a look at some benefits driven by data governance:

Improved Data Quality

Data quality plays an integral role in your organization’s day-to-day decisions, especially if you want to leverage every bit of collected data.

The governance teams make sure the stored data is refined and free from errors. Quality data ensures proper delivery of services to the end-user and better customer satisfaction.

Better analytics are yet another factor that accentuates the importance of good, clean, quality data. With on-the-spot accuracy, you can make decisions due to modern analytic technologies like real-time intelligence and more intelligent AI.

Data Security

Smaller organizations face multiple attacks on their database frequently by malicious groups. Securing your internal and customer data is a huge responsibility for every organization.

Data governance teams formulate multiple checks and authorization points before letting anyone access the data. These policies are regularly updated to ensure there is no data breach or unauthorized access to the data.

Efficiency

Data governance teams make sure that the data is consistent and reliable throughout its life cycle. Less time is wasted in resolving issues arising due to inconsistent data and data standards.

This exponentially decreases the operational costs and increases efficiency in the organization. Data is analyzed swiftly, and services are delivered quickly to the end-user.

Compliance

Data protection laws like General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) are recent initiatives by the global governments to stop the misuse of user data.

These laws are very detailed and include tough clauses to comply with without a good data governance structure in play.

Some data protection laws in certain regions make it mandatory to constitute a governance team to do business in their area.

How Kloudio Can Help with Data Governance

Kloudio, as an organization, can help other companies make datadriven decisions, especially when it comes to meeting and maintaining compliance with data storage and other regulation policies. The idea is to ensure that data is stored securely, and accesses are regularly maintained to avoid any third-party misuse.

Kloudio’s tools are geared towards maintaining data secrecy and efficient report building. Each of their tools facilitate effective data collection, data reporting and data accuracy. To better understand the available products, create a free Kloudio account and see how you can benefit from its usage.

Share this post

Share on facebook
Share on google
Share on twitter
Share on linkedin
Share on pinterest
Share on print
Share on email

Kloudio empowers you to do more with your data

Recent Posts

low code no code

Low Code/No-Code Explained

Low code no code (LCNC) platforms are paving the way for continuous advancement towards a new world of software development. We unpack what each approach means and what tools we recommend.
All articles loaded
No more articles to load
Scroll to Top